CSA Social Marketing: News Clusters
Clustering News Topics
“Which topics are alike in terms of their audiences?”
Correlation Matrix
Optimal Number of Clusters
Hierarchical Clustering
K Means
PC1 = Broad/Systemic Issues (left) vs. Everyday/Current Events (right) PC2 = Large scale (bottom) vs. Local scale (top)
Biplot comparing to news gratifications
PC1: uplift; improvement (left); pragmatic (right) PC2: communal, affective (up); cognitive (down)
Biplot comparing to news characteristics
PC1: creative, interpretive, diverse (left); factual, straightforward (right) OC2: light, entertaining (up); serious (down)
Biplot comparing to demographics
Clustering news gratifications
“Which gratifications are alike in terms of their seekers?”
Optimal number of clusters
Hierarchical clustering
K Means
PC1 = Self-enhacement/affirming (left) vs. Civic-altruistic (right) PC2 = Affective/Moral (Top) vs. Cognitive/Pragmatic (Bottom)
Biplot comparing to news topics
Biplot comparing to news characteristics
Biplot comparing to demographics
Clustering news characteristics
“Which characteristics are alike in terms of their admirers?”